Use of neural network based matched filter for fast response time in high-speed communications channels

Abstract

A neural network is used within a receiver to discriminate a large set of input waveforms without using a very large set of conventional matched filters. The neural network is trained under actual line conditions as opposed to the requirement for ideal signals when using matched filters. The finite waveforms are based on digital modulation principles. A best match is made between a received waveform from the noisy channel and that of previously trained waveforms in order to extract data. Neural network based matched filter allows data be discriminated separately for each sub-carrier channel in the receiver. The neural network system allows fast processing and is suitable for high-speed data communications systems.

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